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SQL for Data Analytics

You're reading from  SQL for Data Analytics

Product type Book
Published in Aug 2019
Publisher Packt
ISBN-13 9781789807356
Pages 386 pages
Edition 1st Edition
Languages
Authors (3):
Upom Malik Upom Malik
Profile icon Upom Malik
Matt Goldwasser Matt Goldwasser
Profile icon Matt Goldwasser
Benjamin Johnston Benjamin Johnston
Profile icon Benjamin Johnston
View More author details

Table of Contents (11) Chapters

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics 3. SQL for Data Preparation 4. Aggregate Functions for Data Analysis 5. Window Functions for Data Analysis 6. Importing and Exporting Data 7. Analytics Using Complex Data Types 8. Performant SQL 9. Using SQL to Uncover the Truth – a Case Study Appendix

Performing Geospatial Analysis in Postgres

In addition to looking at time series data to better understand trends, we can also use geospatial information – such as city, country, or latitude and longitude – to better understand our customers. For example, governments use geospatial analysis to better understand regional economic differences, while a ride-sharing platform might use geospatial data to find the closest driver for a given customer.

We can represent a geospatial location using latitude and longitude coordinates, and this will be the fundamental building block for us to begin geospatial analysis.

Latitude and Longitude

When we think about locations, we often think about it in terms of the address – the city, state, country, or postal code that is assigned to the location that we are interested in. From an analytics perspective, this is sometimes OK – for example, you can look at the sales volume by city and come up with meaningful results...

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